AI & SOCIETY

, 21:395

From judgment to calculation

Original Article

Abstract

We only regard a system or a process as being “scientific” if it displays the three predominant characteristics of the natural sciences: predictability, repeatability and quantifiability. This by definition precludes intuition, subjective judgement, tacit knowledge, heuristics, dreams, etc. in other words, those attributes which are peculiarly human. Furthermore, this is resulting in a shift from judgment to calculation giving rise, in some cases, to an abject dependency on the machine and an inability to disagree with the outcome or even question it. To tolerate such a situation could be seen as an abdication of professional responsibility. In complex technological and scientific environments, it is sometimes said that those who make best use of computers already know what the answer is (in ball park terms) before the calculation.

Keywords

Judgment to calculation Human-centred systems Symbiosis Tacit knowledge Lushai Hills Effect Phylum Rule-following 

References

  1. Cherniak C (1988) Undebuggability and cognitive science. Commun Assoc Comput Mach 31(4):402–412Google Scholar
  2. Cooley M (1991) Architect or bee?: the human price of technology. Chatto & Windus/The Hogarth Press, London. 2nd Impression 1991Google Scholar
  3. Cooley M (1993) Skill and competence for the 21st century. PROC: IITD conference, Galway, April 1993Google Scholar
  4. Cooley M (2002) Stimulus points: making effective use of IT in health. Workshop. Post Grad Department. Brighton & Sussex Medical School 14.10.2002Google Scholar
  5. Cooley M (2005) Re-Joyceing engineers. Artif Intell Soc 19:196–198Google Scholar
  6. Dreyfus HL, Dreyfus SE (1986) Mind over machine. The Free Press 1986Google Scholar
  7. Joseph C (1999) Article, The Times, London 20.04.1999Google Scholar
  8. Mazlish B (1967) The fourth discontinuity. Technol Cult 8(1):3–4CrossRefGoogle Scholar
  9. Mumford L (1963) Technics and civilisation. Harcourt Brace Jovanovich, New York, pp 13–15Google Scholar
  10. Pearson K (1976) Computer power and human reason (cited in Weizenbaum J). WH Freeman & Co, San Francisco, p 25Google Scholar
  11. Polanyi M (1962) Tacit knowing: its bearing on some problems of philosophy. Rev Mod Phys 34(4):601–616CrossRefGoogle Scholar
  12. Press report (2006) Report in Daily Mail 31.05.2006Google Scholar
  13. Rapoport A (1963) Technological models of the minds. In: Sayre KM, Crosson FJ (eds) The modelling of the mind: computers and intelligence. The University of Notre Dame Press, pp 25–28Google Scholar
  14. Rogers L (1999) Article, Sunday Times 18.04.1999, p 7Google Scholar
  15. Rosenbrock HH (1977) The future of control. Automatica 13:389–392CrossRefMathSciNetGoogle Scholar
  16. Rosenbrock HH (1988) Engineering as an art. Artif Inell Soc 2:315–320Google Scholar
  17. Rosenbrock HH (1990) Machines with a purpose. Oxford University Press, Oxford, pp 122–124. (See also Book Review in AI & Society vol 5, no.1)Google Scholar
  18. Rosenbrock HH (2002) USSHER cited in “A Gallimaufry of Quaint Conceits”. Control Systems Centre, UMISTGoogle Scholar
  19. Weizenbaum (1976) Computer power and human reason. WH Freeman & Co., San Francisco, p 25Google Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  1. 1.Technology Innovation AssociatesSloughUK

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